Risto Miikkulainen

Series: Neural Network Modeling & Connectionism

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Risto Miikkulainen draws on recent connectionist work in language
comprehension to create a model that can understand natural
language. Using the DISCERN system as an example, he describes a
general approach to building high-level cognitive models from
distributed neural networks and shows how the special properties of
such networks are useful in modeling human performance. In this
approach connectionist networks are not only plausible models of
isolated cognitive phenomena, but also sufficient constituents for
complete artificial intelligence systems.Distributed neural
networks have been very successful in modeling isolated cognitive
phenomena, but complex high-level behavior has been tractable only
with symbolic artificial intelligence techniques. Aiming to bridge
this gap, Miikkulainen describes DISCERN, a complete natural
language processing system implemented entirely at the subsymbolic
level. In DISCERN, distributed neural network models of parsing,
generating, reasoning, lexical processing, and episodic memory are
integrated into a single system that learns to read, paraphrase,
and answer questions about stereotypical narratives.Miikkulainen's
work, which includes a comprehensive survey of the connectionist
literature related to natural language processing, will prove
especially valuable to researchers interested in practical
techniques for high-level representation, inferencing, memory
modeling, and modular connectionist architectures. Risto
Miikkulainen is an Assistant Professor in the Department of
Computer Sciences at The University of Texas at Austin.